Open-source suite of large-scale simulation environments and benchmarks designed for advancing end-to-end learning in robot navigation and manipulation across multiple embodiments.

Introduces World Action Models (WAMs), a family of 14B-parameter autoregressive diffusion models that jointly predict video and robotic actions to enable zero-shot generalization across manipulation tasks, outperforming fine-tuned Vision-Language-Action models on benchmarks like MolmoSpaces and RoboArena.

An action-conditioned 4D generative robotic simulator that disentangles precise kinematic control from environmental dynamics, facilitating physically-plausible simulations of complex robot-world interactions for training and world modeling.

A plug-and-play framework extracts implicit 3D priors from video diffusion models to enhance multimodal LLMs with spatial reasoning capabilities, enabling improved geometric scene understanding and embodied decision-making without explicit 3D supervision.